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66# Introduction to Machine Learning
## **1. What is Machine Learning?**
**Machine Learning** is a type of Artificial Intelligence where computers learn from data without being explicitly programmed.
Example:
- You give a machine lots of labeled emails (spam or not spam).
- It learns patterns and starts predicting on new emails.
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### **2. Types of Machine Learning**
1. **Supervised Learning**
- Learn from **labeled data** (input → output).
- Example: Predict house prices from size, location, etc.
- Algorithms: Linear Regression, Decision Trees, SVM, Neural Networks
2. **Unsupervised Learning**
- Learn patterns from **unlabeled data**.
- Example: Customer segmentation.
- Algorithms: K-Means, PCA, Hierarchical Clustering
3. **Reinforcement Learning**
- Learn by **interacting with an environment** and getting rewards or penalties.
- Example: Training a robot to walk or an AI to play chess.
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### **3. Basic Steps in an ML Project**
1. **Collect Data** – Get a dataset relevant to your problem.
2. **Preprocess Data** – Clean, normalize, and format your data.
3. **Choose a Model** – Pick an algorithm to try.
4. **Train the Model** – Feed it data to learn.
5. **Evaluate the Model** – Test on new data.
6. **Tune & Improve** – Adjust parameters, try better features or algorithms.
7. **Deploy** – Use it in a real-world app.
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### **4. Popular Tools & Libraries**
- **Python** (most popular ML language)
- Libraries:
- **Scikit-learn** – Simple, powerful for beginners
- **Pandas & NumPy** – Data manipulation
- **TensorFlow / PyTorch** – Deep learning
- **Matplotlib / Seaborn** – Visualization
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### **5. Key Concepts**
- **Features** – Inputs to the model (e.g., age, salary)
- **Labels** – What you’re predicting (e.g., job title)
- **Overfitting** – When your model memorizes training data
- **Underfitting** – When your model is too simple
- **Accuracy, Precision, Recall, F1 Score** – Evaluation metrics
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Want a hands-on mini-project or a roadmap to go deeper next?